Optimizing MS Parameters for Data-Independent Acquisition (DIA) to Enhance Untargeted Metabolomics

Data-Independent Acquisition (DIA) has emerged as a powerful
mass
spectrometry (MS) strategy for comprehensive metabolomics. This study
presents a novel short gradient (13 min) nanosensitive analytical
method for human plasma analysis using DIA LC-MS/MS, focusing on in-depth
optimization of MS parameters to maximize data quality and metabolite
coverage. Key MS parameters, including scan speed, isolation window
width, resolution, automatic gain control, and collision energy, were
systematically tuned to balance the sensitivity and specificity while
minimizing interferences. The optimized method enabled the detection
of 2,907 features with 675 annotated compounds, leveraging recent
progress in nano-LC-MS/MS for multiomics applications and showcasing
the possibility of combining proteomics and metabolomics within a
single chromatographic system. Ultimately, a comparison was performed
between the data acquired through the DIA and DDA MS approaches in
the context of untargeted metabolomics. This optimized analytical
method yields more robust and reproducible results, thereby expanding
the potential for meaningful discoveries across diverse biological
fields.